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Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms

In the living cells, proteins bind small molecules (or “ligands”) through a “conformational selection” mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine le...

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Detalles Bibliográficos
Autores principales: Gupta, Shivangi, Baudry, Jerome, Menon, Vineetha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025728/
https://www.ncbi.nlm.nih.gov/pubmed/35458706
http://dx.doi.org/10.3390/molecules27082509
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author Gupta, Shivangi
Baudry, Jerome
Menon, Vineetha
author_facet Gupta, Shivangi
Baudry, Jerome
Menon, Vineetha
author_sort Gupta, Shivangi
collection PubMed
description In the living cells, proteins bind small molecules (or “ligands”) through a “conformational selection” mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of “binding protein conformations” in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such “binding conformations” for proteins and provides a big data approach to the conformational selection mechanism.
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spelling pubmed-90257282022-04-23 Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms Gupta, Shivangi Baudry, Jerome Menon, Vineetha Molecules Article In the living cells, proteins bind small molecules (or “ligands”) through a “conformational selection” mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of “binding protein conformations” in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such “binding conformations” for proteins and provides a big data approach to the conformational selection mechanism. MDPI 2022-04-13 /pmc/articles/PMC9025728/ /pubmed/35458706 http://dx.doi.org/10.3390/molecules27082509 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gupta, Shivangi
Baudry, Jerome
Menon, Vineetha
Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms
title Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms
title_full Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms
title_fullStr Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms
title_full_unstemmed Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms
title_short Using Big Data Analytics to “Back Engineer” Protein Conformational Selection Mechanisms
title_sort using big data analytics to “back engineer” protein conformational selection mechanisms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025728/
https://www.ncbi.nlm.nih.gov/pubmed/35458706
http://dx.doi.org/10.3390/molecules27082509
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